ML-MovieRecommend:

Predicting Movie Ratings by using Matrix Factorization:

A full stack web project built with:

- Frontend - React (:3000)

- Backend - .NET Web API (:7033)

- Machine Learning - ML.NET Library

- Dataset - MovieLens Movie Database

Steps used by the Matrix Factorization Algorythm in the MLService:

1. The LoadDataFromDb method reads the training data from a CSV file and returns an IDataView. The schema for the training data is defined by type MovieRating.
2. The TransformData method encodes the two features userId and movieId. These encoded features will be provided as input to the MatrixFactorizationTrainer.
3. The GenerateOptionsObject method specifies the options for the MatrixFactorizationTrainer.
4. The GeneratePipeline method creates the training pipeline.
5. The TrainAndReturnModel method creates the training pipeline by appending the data processing pipeline and MatrixFactorizationTrainer. It fits the model to the training dataset and returns the trained model.
6. The EvaluateModelPerformance method loads the test dataset, performs the prediction, and evaluates the model's performance. The model evaluation metrics include RootMeanSquaredError.
7. The GetMovieRatingPrediction method makes a single movie rating prediction for a specific user. It uses the trained model to predict the rating for a particular movie for a user.

Screenshots:

Examples of the Datasets Used:

recommendation-movies.csv (9743 movies)

movieId	title	genres
1	Toy Story(1995)	Adventure Animation Children Comedy Fantasy
2	Jumanji (1995)	Adventure Children Fantasy
3	Grumpier Old Men (1995)	Comedy Romance

recommendation-ratings-test.csv (20 records – just used as test values)

userId	movieId	rating	timestamp
1	1097	5	964981680
1	1127	4	964982513
1	1136	4	964981327

recommendation-ratings-train.csv (62369 records – used to train the model)

userId	movieId	rating	timestamp
1	1	4	964982703
2	3	3	964981247
3	4	5	964982224